Integrated monitoring, modeling, assessment of highly-migratory fish populations using spatially-explicit, ecological-based prediction models, artificial intelligence, and big data: The Atlantic bluefin tuna (Thunnus thynnus) is a long-lived, highly migratory species that attain sizes of 2.20 m, weights of 650 kg or more, and live over 32 years. Adults undertake cyclic migrations between coastal feeding zones, offshore wintering areas and spawning grounds. During June through October, bluefin tuna are common off the eastern United States and Canada, entering the Gulf of Maine/Gulf of St. Lawrence, a semi-enclosed continental shelf area. Bluefin migrate seasonally up to this region as adults from the Gulf of Mexico, which is the only known spawning area for the Western Atlantic population. Tagging studies reveal bluefin or many different ages regularly undertake large-scale migrations (7400 km or more) across the Atlantic. The International Commission for the Conservation of Atlantic Tunas (ICCAT) has adopted an interim conservation and management plan for 2018-2020, having been unable to determine population status following a 20-year rebuilding plan. The recent NOAA stock assessment (2017) indicates that the western Atlantic population is not being overfished, but has an unknown overfished status, with spawning stock biomass increasing since 2004. Spatial population assessments and models of Atlantic bluefin are primarily based on catch rate data, with assumptions that are typically insufficient for capturing real temporal and spatial shifts in their distribution, especially under large inter-annual biotic and abiotic variability and uncertainty. Reliable current and future population estimates incorporating new fishery-independent indices are therefore urgently needed to better explain and track changes in seasonal abundances and trans-Atlantic stock mixing. Using such indices, a new operational management strategy for this species could be designed that is linked to a broader evidence based involving “Big data” from vessel, airborne, and satellite-based monitoring data. This new strategy could incorporate more realistic and robust assumptions linked with their behavioral ecology (schooling, finding prey, preferred ocean conditions) and changing ocean conditions due to climate change. In this presentation, I provide a summary of modeling research work in modeling of bluefin tuna population dynamics - from individual to school to population scales – which revealed new information and insights on their movement and behavior. This work developed a novel statistical population assessment approach and a spatially-explicit individual-based prediction model integrating new information from tracking, tagging, and survey data.

1. AtlanticBluefinTuna:
Ecological-basedmanagementofhighlymigratorytuna
usingartificialintelligenceandbigdata
Nathaniel K. Newlands
Research Scientist
(Sustainable Ecosystems)
Government of Canada,CANADA
nathaniel.newlands@canada.ca

2. International management (ICCAT)
• Two-stock hypothesis usingVPA based on catch-at-age statistics
(distinct western/eastern stocks separated by a management
boundary at the 45oW meridian)
• 2018-2020 Interim conservation and management plan
(unable to determine population status following a 20-year
rebuilding plan enacted in early 1980s)

3. Separate spawning zones, shared feeding zones
• Spawning site-fidelity
• Migrations depend on fish age and size,
which are mainly related to reproduction
and the search for food
• Resident population in eastern Mediterranean
based on pop-up satellite tagging
• Meta-populations occupying
different habitats and having
degree of influence over one
another (Fromentin & Powers, 2005)
• Even low movement rates exerts
significant influence on the abundance
and stock composition

4. ICCAT stock composition database
• Catch-at-age, Mixed stock CPUE: Population-of-origin (n=6886), assignment probability varies widely
• West (1974-2015, ages 1-16+,n=2773), East (1968-2015, ages 1 to 10+, n=2727)
• Significant differences in the relative abundance
• Eastern population an order of magnitude greater than the western
• Significant variability in western mixed-stock and western-origin indices
Source: Morse et al., (2018) An updated analysis of bluefin tuna stock mixing. Collect.Vol. Sci. Pap. ICCAT, 74(6): 3486-3509
Estimated eastern proportions by year and fleet (traps, longline, bait boat, rod & reel)

5. International management (ICCAT)
• Recent 2017 NOAA stock assessment indicates that the western Atlantic population is not
being overfished, but has an unknown overfished status, with spawning stock biomass
increasing since 2004
• Characterizing stock composition and the effects of stock mixing is a priority for improving
assessment and management
• Stock mixing violates the “unit stock” assumption of virtual population analysis (VPA) and
statistical age-structured assessment models to the separate eastern and western stocks
(ICCAT 2017)
• Currently…
- no error in the estimates of stock composition
- no error in the estimates of catch-at-age (VPA assumption)
- CPUE trends andVPA’s in-sensitive to time-constant stock composition assumptions
Source: ICCAT. 2017. Report of the 2017 ICCAT Bluefin Stock Assessment Meeting (Madrid, Spain – July 20 to 28, 2017).

6. Strategic framework (fishery-independent assessment)
Source:
Newlands, N.K., Lutcavage, M.E., Pitcher,T.J. (2006) Atlantic bluefin tuna in the Gulf of Maine, I: estimation of seasonal abundance accounting for movement, school and school-
aggregation behavior. Environmental Biology of Fishes, 77(2): 177-195.
Lutcavage, M. and Newlands, N.K. (1999) Strategic framework for fishery-independent aerial assessment of bluefin tuna. Collect.Vol. Sci. Pap. ICCAT (SCRS), 49(2): 400-402.

7. Foraging movements (behaviour modes)
Source:
Newlands, N.K., Lutcavage, M.E., Pitcher,T.J. (2004) Analysis of foraging movements of Atlantic bluefin tuna (Thunnus thynnus):
Individuals switch between two modes of search behavior. Population Ecology, 46:39–53.

8. Migration movements (light archival tagging)

9. Diffusion scaling: foraging vs. migration

10. Schooling dynamics
Source:
Newlands, N.K, Porcelli,T.A. (2008) Measurement of the size, shape and structure of Atlantic bluefin tuna schools in the open ocean. Fisheries Research, 91: 42–55

11. Meta-populations: school aggregation to prey

12. Aerial surveying
• Benchmark survey precision of 4–7% (bias and variance-corrected spotter pilot data)
• Estimating seasonal abundance: (1,301–3,302)%, accounting for tuna behavior
• If survey design improved: 82–93%
• Adaptive survey design: can achieve 10–50% for a 3–8 year duration

13. Aerial surveying

14. Spatial models
Sources:
Kerr, L.A., Cadrin, S. X., Secor, D. H. Nathan G.Taylor. 2016. Modeling the implications of stock mixing and life history uncertainty of Atlantic bluefin tuna.Canadian Journal of
Fisheries and Aquatic Sciences, DOI: 10.1139/cjfas-2016-0067.
Taylor N., McAllister, M., Lawson, G., Carruthers,T. and Block, B. 2011. Atlantic BluefinTuna:A Novel Multi-stock Spatial Model
for Assessing Population Biomass. PLoS ONE 6(12):e27693.doi:10.1371/journal.pone.0027693
ICCAT. 2008. Report of the 2008 Atlantic BluefinTuna Stock Assessment Session. (Madrid,Spain – June 23 to July 4, 2008).

15. Spatially-explicit IBM population dynamics model (SIBM)

16. SIBM model continued…
Model vs survey data (1994-96)
Evolutionary strategy: foraging success, predation risk
Simulated on an oceanographic grid (trophic and ocean data)

17. Take home message “need a more robust approach (ecology and big data)”
• From MSY target to dynamic economic yield (MEY) for meeting both conservation and economic
objectives (Jules et al., 2018)
• MEY strategy converges to SSB steady-state that is well above SSB at the MSY for all recruitment and
global scenarios, robust to stock estimation strategies
• Spatially-explicit “Bayesian” approach integrating movement, schooling, prey and ocean gradients,
climate change forcing
• Integrate “Big data” from vessel, airborne, and satellite-based monitoring data
• Project Eyes on the Sea and Global FishingWatch usingAutomated Identification System (AIS),
AI pattern-detection algorithms and Big data (Vessel-tracking) (Google with Oceana and Skytruth)
• Big data is not enough to stop overfishing : many boats don’t have AIS or don’t turn it on, undetectable
illegal fishing off Palau/Pacific island nations
• Future of fishing isAI and big data….need earth observation/satellite information
(trophic ecosystem/prey aggregation habitats, sea-surface temperature (SST) etc..)
• Reduce uncertainties, broader testing of spatial- and ecologicaly-based assumptions
(not considered inVPA).
Sources:
Selles, J., Bonhommeau, S., Guillotreau, P. (2018) Optimal bioeconomic management of the Eastern Atlantic Bluefin tuna fishery:
where do we stand after the recovery plan? FAEREAnnualConference. http://hal.archives-ouvertes.fr/hal-02048714

18. Management stock structure - Pacific Bluefin tuna